Search Results (909)

Value co-creation among diverse actors in digital innovation ecosystems (DIEs) exhibits characteristics of high complexity and dynamic evolution. Grounded in the Quadruple Helix Theory, this study develops a conceptual model that interlinks “supervisory guides, knowledge providers, technology transformers, and user demand parties.” This model is defined by organizational oversight as its nexus, knowledge and technology as its foundation, outcome transformation as its core, and user needs as its orientation. Building upon this conceptual foundation, we establish a four-party evolutionary game model involving “innovation regulators (government), innovation producers (academic/research institutions), innovation decomposers (enterprises), and innovation consumers (users).” This analytical framework is then applied to systematically investigate the dynamic evolutionary mechanisms and collaborative pathways for value co-creation in DIEs. We construct the payoff matrix and replicator dynamics to derive the system’s Evolutionarily Stable Strategies (ESSs). Numerical simulations via MATLAB R2023b identify the stability conditions for each party’s strategic choices and unravel the influence mechanisms of key parameters. The results demonstrate nine distinct ESSs, categorized into three types: low-level stability, regulation-dominated transitional stability, and high-level cooperative stability. While the agents’ initial strategies do not alter the system’s final equilibrium state, they significantly impact the speed of evolutionary convergence. Critical factors—including regulators’ intervention costs, subsidy and penalty mechanisms, producers’ and decomposers’ cooperation and default costs, and consumer feedback behaviors—collectively drive the system toward the ideal (1, 1, 1, 1) equilibrium. Theoretically, this study enriches the perspective on multi-agent collaboration in value co-creation by introducing a dynamic quantitative analytical framework, thereby addressing a notable gap in the literature. Practically, it provides actionable insights for mechanism design and a solid foundation for policy optimization, aiming to foster a synergistic governance system that integrates “regulatory guidance, market incentives, and social feedback.” Full article

In the Internet of Vehicles, vehicular crowdsensing is crucial for alleviating traffic congestion and ensuring the safety of autonomous driving. However, practical vehicular crowdsensing processes face dual challenges of skewed spatial distributions of vehicles and inadequate data quality guidance. These issues cause sensing redundancy in high-participation areas (HPAs) and coverage deficits in low-participation areas (LPAs), while also leading to unstable data quality. Given that participants’ decisions are profoundly influenced by bounded rationality and psychological preferences, this paper proposes a collaborative incentive mechanism integrating behavioral economics and psychology (BEP-IM) to drive sustained spatial coverage and proactive sensing shaping. First, to mitigate coverage deficits in LPA, a reference-dependent two-sided selection and bidding strategy (RD-TSB) is designed to guide participants toward LPA via a reference-driven utility evaluation. Concurrently, a loss-aversion-based sustained incentive strategy (LA-RPI) is introduced to enhance their sustained participation within LPAs by amplifying loss perception. Furthermore, to overcome weak data quality constraints, an operant conditioning-based proactive sensing shaping strategy (OC-SFQ) is constructed, utilizing a closed-loop mechanism of relative improvement, variable-ratio reinforcement, and association updating to drive participants to output high-quality data. Simulation results demonstrate that the proposed mechanism effectively increases participation frequency in LPAs and optimizes sensing data quality. Full article

The construction industry is one of the major sources of carbon emissions, and green retrofitting of buildings is an effective pathway to promoting sustainable development in the sector. However, existing research and implementation strategies often struggle to reconcile the needs of governments, businesses, and residents. Therefore, this study proposes a comprehensive research framework that employs bibliometric and text analysis methods to examine implementation barriers in retrofitting projects across four dimensions: policy, cost, technology, and resident satisfaction. The results indicate that retrofitting costs are the primary factor, while technology is a secondary factor. Furthermore, existing policies feature vague technical standards, insufficient incentives, and a lack of differentiation. Conflicts of interest and challenges regarding cost allocation persist throughout the renovation life cycle. Decision-support tools and renovation technologies face limitations and issues regarding applicability. Residents face constraints from multiple factors, including their knowledge base and economic capacity. Based on these findings, the government urgently needs to improve a differentiated policy system and encourage technological R&D and knowledge dissemination. Enterprises must actively respond to policies and optimize their technologies and management practices. Residents need to enhance their energy-saving awareness, participate in retrofitting efforts, and improve their energy consumption behaviors. Full article

During the critical period of agricultural green transformation, clarifying the evolutionary logic of farmers’ green production behavior under a multi-stakeholder framework provides significant insights for implementing “Dual Carbon” goals, establishing long-term mechanisms for high-quality agricultural development, and resolving deep-seated contradictions in agricultural non-point source pollution. Based on the social co-governance and public participation framework, this paper constructs a tripartite evolutionary game model involving government departments, farmer groups, and the general public, grounded in cost–benefit analysis, social governance friction, and evolutionary game theory. Through simulation, the study explores the equilibrium states and the specific impacts of varying parameter values on stable points. The findings reveal that: (1) The “interest price scissors” (benefit disparity) between green and conventional production is the key determinant of farmers’ strategic equilibrium. Once this structural contradiction is resolved, green production becomes the optimal strategy. (2) Farmers are highly sensitive to marginal cost–benefit fluctuations, leading to a sequential behavioral cascade: farmers retreat first, followed by the government, and finally the public. (3) Public participation cost is the pivotal variable for activating the co-governance mechanism, and the application of digital governance tools determines the time required to reach equilibrium. (4) A “Success Paradox” exists in government regulation; incentive mechanisms must be adjusted promptly after initial success. (5) Integrated policy combinations outperform single instruments; breaking the “locked-in” state requires a policy shock of sufficient intensity. This research offers a theoretical basis and policy enlightenment for optimizing the social co-governance landscape and promoting sustainable agricultural modernization. Full article

To address the bottlenecks of low sample efficiency and poor control accuracy in traditional single-layer reinforcement learning during autonomous driving overtaking, this paper proposes an overtaking decision and control system based on hierarchical reinforcement learning to decouple complex tasks in spatial and temporal dimensions. A heterogeneous two-layer architecture is constructed, where the upper layer adopts the Proximal Policy Optimization algorithm to generate macroscopic discrete decisions, while the lower layer employs Twin Delayed Deep Deterministic Policy Gradient combined with Long Short-Term Memory to achieve smooth continuous control of steering and acceleration by perceiving temporal features of dynamic obstacles. A composite reward mechanism, integrating hard safety constraints and soft efficiency incentives, is designed to balance safety, efficiency, and comfort. Experimental results in complex scenarios with multiple interfering vehicles and random lane-changing behaviors demonstrate that the proposed system improves the training convergence speed by approximately 30% within 500,000 steps compared to single-layer algorithms. In tests across varying traffic densities, the system achieves a 98.3% success rate in medium-density scenarios with a collision rate of only 0.6%. In high-density challenges, the success rate remains above 95%, with the collision rate reduced by about 80% compared to baseline models. Furthermore, the lateral control deviation is strictly limited to within 0.2 m, and the longitudinal safety distance remains stable above 5 m. This system provides a robust, high-efficiency paradigm for autonomous overtaking. Full article

Under fault conditions, Photovoltaic-Storage-Charging Integrated Stations (PSCISs) are regarded as a key resource for enhancing distribution network resilience. However, traditional centralized optimization fails to account for conflicts of interest between the distribution network and PSCISs and neglects the actual response behavior of EV users. To address these issues, a coordinated emergency operation strategy for distribution networks and PSCISs based on the master–slave game is proposed. Firstly, a bilevel optimization framework based on the master–slave game is constructed, where the upper level performs system-level coordination and the lower level handles autonomous decision-making. For the upper level, the minimization of distribution network operation cost is set as the optimization objective by the dispatching center to determine power purchase prices and load shedding rates, which serve as guidance signals for lower-level PSCISs. In terms of the lower level, a dual-factor S-shaped response curve is introduced into the lower-level model to precisely characterize EV users’ nonlinear response behavior to price incentives. Furthermore, based on the signals received from the upper level, the maximization of each PSCIS’s profit is set as the optimization objective to determine the PV output, storage dispatch, and V2G incentive prices. Subsequently, Model Predictive Control (MPC) is employed to implement rolling optimization during the fault period, addressing the source-load uncertainties. Finally, an improved IEEE 33-node distribution network is used for case analysis and validation of the proposed operation strategy. The results indicate that the proposed strategy can effectively coordinate the interests of multiple parties, achieving synergistic improvements in both the economy and reliability of the distribution network. Full article

Those who serve in the armed forces are shaped not only by incentives and opportunity structures but also by institutions that cultivate norms of duty, authority, and collective obligation. This study argues that religious institutions function as such socializing agents and play a measurable role in military enlistment in the United States. Complementing existing research that focuses on denomination or belief as key indicators, we introduce an institutional framework that emphasizes participation in religious communities. The focus is not on the affiliation but instead on the socialization offered and conducted in those institutions. Religious communities cultivate behavioral dispositions, such as discipline, hierarchy, and collective orientation, that align with the demands of military service. As such, they are associated with an increased likelihood of enlistment. Using data from the 2024 Cooperative Election Study (CES), we employ logistic regression models to distinguish between religious identity, institutional engagement, and individual religiosity. The results show that, per our sample, religious identity and evangelical affiliation are not significant predictors of enlistment. Instead, regular participation in religious institutions is strongly and consistently associated with a higher likelihood of military service. These findings suggest that institutional socialization can be an important factor in explaining the relationship between religion and military service. Full article

Promoting the effective use of abandoned farmland has become a key policy priority for strengthening food security in China. However, disentangling the decision-making processes among diverse participating actors is a foundational prerequisite for addressing the governance challenge of abandoned farmland utilization. Building on this, the present study employs a four-actor evolutionary game model and sensitivity analysis of key parameters to systematically examine the interactions among four key actors—local governments, village collectives, family farms, and farmers—and to identify the corresponding evolutionarily stable strategies (ESSs) across different stages of abandoned farmland utilization. The results show that: (1) Multi-actor strategic interactions in abandoned farmland utilization exhibit a multi-stage evolutionary trajectory, in which all actors gradually shift their strategic choices under changing cost–benefit structures, regulatory intensity, and coordination conditions, leading to different evolutionary stable equilibria across governance stages. (2) The configuration in which local governments adopt loose regulation, the village collective plays an active coordinating role, family farms pursue long-term operations, and farmers choose recultivation is a key condition for achieving a Pareto-optimal equilibrium. (3) Although farmers’ production willingness and behavioral choices form the basis for the utilization of abandoned farmland, spontaneous individual action alone is insufficient to address the structural contradictions currently facing abandoned farmland utilization in China. To effectively promote the evolution of abandoned farmland governance toward a stable collaborative equilibrium and ultimately realize sustainable utilization, it is necessary to further optimize governmental administrative control models and incentive mechanisms, strengthen the organizational and coordinating functions of village collectives, and improve long-term operational support systems for family farms. This study systematically elucidates the underlying logic of China’s abandoned farmland utilization from the perspective of multi-actor behavioral decision-making, providing policy-referential insights for optimizing policy design, reducing coordination costs, and improving the efficiency of abandoned farmland utilization. Full article

Mobile data collectors (MDCs) play a very important role in Internet of Things (IoT) sensing networks. However, ensuring their trustworthiness against insider threats, such as on–off attacks and spatiotemporal fabrication, remains a critical challenge. Existing trust evaluation methods frequently struggle with these threats due to insufficient evidence dimensions and the inability to quantify behavioral stability. To address these limitations, this paper proposes an enhanced proactive trust evaluation system based on stability sequence extraction (E-PTES-S). E-PTES-S improves the evaluation accuracy by integrating five factors of evidence, stability-computation mechanisms, and an adaptive weight allocation scheme to maintain robustness even when proactive verification data is scarce. In addition to the usual interaction and proactive verification indicators, regional consistency (${\mathcal{T}}_{RC}$) and task timeliness (${\mathcal{T}}_{TT}$) are introduced to mitigate location falsification and transmit-time deviations more rigorously. Then, a sliding window technique is used to obtain an integrated evidence sequence, which includes a new continuous stability sequence (${\mathcal{F}}_{CSS}$) and traditional credible, untrustworthy, and uncertain sequences. This continuous stability sequence adds a variance-based incentive scheme to measure behavioral stability. Finally, the normalized trust value is derived from multiple indicators including multidimensional spatiotemporal evidence and stability metrics. Experimental results show that the proposed E-PTES-S achieves a normal node detection rate of 98.7% under complex dynamic conditions, outperforming the baseline PTES and Trust-SIoT algorithms by approximately 9% and 1%, respectively, while also improving the cumulative data collection profit by 4.8%. Furthermore, robustness analysis demonstrates that E-PTES-S exhibits excellent robustness against physical-layer uncertainties, successfully sustaining an 84.4% detection rate even under severe environmental shadowing. Full article

Logistics platforms (LPs) increasingly use multidimensional data to provide supply chain financing (SCF) to small and micro logistics enterprises (SMLEs). However, platform-centered data control can weaken financial institutions’ (FIs’) trust in platform data, thereby reducing the effectiveness of data-driven credit enhancement. To address this issue, this study integrates the social–ecological systems framework with evolutionary game theory and develops a tripartite evolutionary game involving FIs, LPs, and SMLEs. By comparing scenarios with and without regulatory governance, the study examines how regulatory governance affects the strategic evolution of data-driven credit enhancement in SCF for SMLEs. The results show that regulatory governance improves system performance through cost reduction, trust enhancement, and incentive alignment, thereby relaxing the conditions required for the system to evolve toward the Pareto-optimal state of credit granting, strict supervision, and non-default. The strategic choices of the three actors are mainly influenced by data acquisition costs, incentive intensity, and penalties. Numerical simulations further show that government incentives must exceed certain thresholds to promote cooperation, while penalty mechanisms play a critical role in constraining opportunistic behavior and accelerating convergence to the desirable equilibrium. These findings provide theoretical support and practical insights for improving data-driven credit enhancement in SCF for SMLEs. Full article

Systemic digital innovation plays a pivotal role in driving firms’ future growth. As key decision-makers in strategic planning, executives play a critical role in promoting digital innovation. Therefore, how to effectively motivate executives to engage in systemic digital innovation remains an important research question. Drawing on principal-agent theory, this study examines how equity incentives promote systemic digital innovation, a form of firm-level digital technological innovation embedded in organizational governance and resource allocation systems. Using the panel data from Chinese A-share listed firms over 2007–2024, we investigate the governance mechanisms in a major emerging market context. The results show that equity incentives significantly promote systemic digital innovation. Managerial risk-taking and long-term orientation partially mediate this relationship, indicating that incentive alignment reshapes executives’ behavioral orientations toward intertemporal decision-making. Moreover, executives’ IT background strengthens the positive effect of equity incentives, whereas financing constraints weaken it. These findings highlight equity incentives as a governance mechanism that facilitates sustained systemic digital innovation in emerging market firms. Full article

Digital urban–rural sharing of social public resources (SPRs) is important for improving resource allocation efficiency and narrowing urban–rural disparities. This study applies a tripartite evolutionary game framework to analyze the strategic interactions among the government sector, the sharing supply side, and the sharing demand side in the digital urban–rural SPR sharing process. A system dynamics (SD) model is further constructed to simulate the dynamic evolution of the system under different initial conditions and parameter settings. The results show that the system generally evolves along a path of government initiation, demand-side response, and supply-side follow-up. Higher collaborative benefits, lower resource transfer costs, stronger government credibility, and appropriately designed subsidies promote active sharing and accelerate convergence toward a high-sharing stable outcome. In contrast, high transfer costs, weak collaborative incentives, and insufficient regulatory credibility inhibit sharing behavior or delay convergence. In addition, different initial cooperation levels mainly affect the convergence speed and fluctuation pattern of the evolutionary process. This study extends the application of the tripartite evolutionary game framework to the digital urban–rural SPR sharing context and combines it with SD simulation to reveal the system’s dynamic evolution mechanism. The findings provide practical implications for promoting digital urban–rural SPR sharing through moderate subsidies, reduced transfer costs, enhanced regulatory credibility, and strengthened collaborative mechanisms. Full article

This study addressed a critical gap in understanding the drivers of movie consumption during digital transformation and streaming platform proliferation. It examined the direct effects of three core dimensions—social value, functional value, and emotional value—on movie consumption behavioral intention, alongside the mediating mechanism of satisfaction. Data were collected via questionnaire surveys administered to cinema audiences in Eastern China and through Wenjuanxing online platform, yielding 1089 valid responses. Statistical analysis was conducted using SPSS 26.0, and Structural Equation Modeling (SEM) was performed employing AMOS 26.0. Findings indicate significant positive direct effects of social value and emotional value on movie consumption behavioral intention. Furthermore, these value dimensions indirectly enhance movie consumption behavioral intention through the mediating influence of satisfaction. In contrast, functional value demonstrates no significant direct effect on either movie consumption behavioral intention or satisfaction. Satisfaction serves as a significant mediator in the relationships between both social value and emotional value, and movie consumption behavioral intention. This study elaborated the distinct pathways through which varied perceived value dimensions operate and empirically validates the mediating role of satisfaction within movie consumption decision-making. For the movie industry, these insights suggest prioritizing social engagement and emotional resonance to optimize offerings, establishing dynamic satisfaction monitoring, and designing member incentives targeting these values to foster sustained behavioral activation. This provides empirically grounded guidance for refining marketing strategies and experiential enhancements. Full article

Against the dual backdrop of worsening air pollution and industrial intelligent transformation, industrial robot technology has become an important means to promote air pollution synergistic governance. This study innovatively incorporates public environmental satisfaction and industrial robot application as dynamic mechanism variables, constructing an evolutionary game model involving the government, industrial enterprises, and the public. Through theoretical analysis and numerical simulation, the study reveals the influence mechanism of key cost–benefit parameters on stakeholders’ strategic interaction and the system’s evolution path. The conclusions are as follows: (1) The government’s environmental supervision directly affects enterprises’ green transformation willingness, and enterprises’ behavior reversely impacts public satisfaction and supervision effectiveness, forming a “supervision–response–feedback” closed-loop. (2) The cost and benefit parameters related to industrial robots are crucial for the evolution of the game system, and there is significant heterogeneity in their impact on the strategic choices of the three parties. The robot adaptation transformation of enterprise industrial depends on the comprehensive consideration of the transformation cost and the green benefits. Public supervision is regulated by both the supervision cost and the incentive benefit. The government regulation takes into account both the regulatory cost and the loss of social reputation. Various parameters dynamically regulate the system’s equilibrium by altering the party’s cost–benefit structure. (3) The application of industrial robots and the feedback of public environmental satisfaction form a coupling effect, jointly determining the long-term evolution direction of the game system. When the cost benefit and supervision incentives are well-matched, enterprises will actively promote the green transformation of industrial robots in order to achieve intelligent pollution control. The effectiveness of public supervision has also been fully realized. The dynamic adaptation of the two components can lead the system towards an efficient and stable equilibrium in air pollution governance. Full article

The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle to reconcile individual user preferences with sustainable consumption objectives, frequently encountering the “information cocoon” effect and lacking interpretability in their decision-making processes. To address these issues, this study proposes a multi-objective, context-aware hotel recommendation framework that integrates text mining, sequential behavior modeling, and reinforcement learning. The framework begins by employing unsupervised learning to extract multidimensional hotel features from online reviews, with an explicit emphasis on comprehensive sustainability metrics. It subsequently applies a dynamic state representation approach that merges long-term and short-term interests with real-time contextual information to accurately reflect evolving consumer needs. Furthermore, a dynamic feature weighting module is incorporated to enhance interpretability and enable context-adaptive evaluation of both commercial and sustainable attributes. The recommendation process is structured as a Markov Decision Process, leveraging a composite reward function comprising diversity penalties and sustainability incentives. Empirical analysis using real-world data validates the framework, demonstrating its contribution to sustainable tourism and achieving recommendation accuracy that surpasses existing benchmark models. Full article